Pytraj, nglview: bring AmberTools to Python ecosystem¶
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from IPython import display
display.Image('team.png', width=700)
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Slides: github.com/hainm/amber_meeting_2016
pytraj: github.com/Amber-MD/pytraj
Workflow¶
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display.Image('./old_work_flow.jpg')
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Simpler workflow?
Not a professional programmer but still want to write cool analysis in Python and make it as fast as c++? (ask Dan?)
Read new trajectory format that cpptraj does not support? (write XTC file parser with 3 lines of codes?)
Write parallel code for analysis? (Hint: how we can make parallel calculation with only one line of code?)
Interface with other programs? (pyrosetta, rdkit, cclib, …)
Is Python really slow?¶
Nope
from numba import jit
from numpy import arange
# jit decorator tells Numba to compile this function.
# The argument types will be inferred by Numba when function is called.
@jit # make the code as fast as C/C++
def sum2d(arr):
M, N = arr.shape
result = 0.0
for i in range(M):
for j in range(N):
result += arr[i,j]
return result
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# data: http://www.amber.utah.edu/AMBER-workshop/London-2015/DNA-tutorial/
import pytraj as pt
traj0 = pt.load('md.nc', 'dna.prmtop')
traj0
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traj = traj0.autoimage()['!:WAT']
traj
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# compute rmsd and convert raw data to pandas' DataFrame
data = pt.rmsd(traj, ref=0, mask=':1-24&!@H=', dtype='dataframe')
data.head(10)
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%matplotlib inline
data.plot()
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data.hist()
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Parallel¶
- openmp (through cpptraj)
- multiprocessing
- mpi
- mpi + openmp?
- cpptraj, cpptraj.OMP, cpptraj.MPI: Nope
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traj = pt.iterload(['tz2.0.nc', 'tz2.1.nc', 'tz2.2.nc'], 'tz2.parm7')
# multiprocessing
data = pt.pmap(pt.rmsd, traj, ref=0, mask='@CA', n_cores=8)
# MPI
# data = pt.pmap_mpi(pt.rmsd, traj, ref=0, mask='@CA')
# run: mpirun -n 8 python your_code.py
# serial: data = pt.rmsd(traj, ref=0, mask='@CA')
Crazy idea: Write code in serial and make it run in parallel¶
def method(traj, mask, ...):
for frame in traj:
dosome_thing_fun(frame)
...
return data
pt.pmap(method, traj, mask, ..., n_cores=8)
# TB of data? no problem
# write serial code in Cpptraj? no problem
Parallel scaling¶
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from IPython import display
display.Image('bench_pmap_casegroup.png', width=600)
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Intergrate will pysander (Python interface for sander)¶
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traj2 = pt.iterload('tz2.nc', 'tz2.parm7')
energies = pt.energy_decomposition(traj2, igb=8, dtype='dataframe')
energies[['bond', 'angle', 'dihedral', 'gb']].head(6)
# wanna make the caculation faster?
# energies = pt.pmap(pt.energy_decomposition, traj2, igb=8, n_cores=8)
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Writing new file parser with 3 lines of code?¶
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# XTC
# amber.conda install mdtraj # 10 second to install
import mdtraj as md
t0 = md.load('monolayer.xtc', top='monolayer.pdb')
coordinates = t0.xyz.astype('f8')
traj = pt.Trajectory(xyz=coordinates, top='monolayer.pdb')
pt.center_of_mass(traj)
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Document?¶
Non official manual: amber-md.github.io/pytraj
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help(pt.calc_center_of_mass)
NGLVIEW - Trajectory viewer in Jupyter notebook¶
- light viewer for Jupyter notebook (~2 MB source code)
- beautiful rendering
- fast (WebGL)
- super easy to install
amber.pip install nglview
- able to view TB of data (I mean it, but should use netcdf format)
- able to plug any backends (pytraj, mdtraj, ParmEd, ...)
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display.Image('./3pqr_nglview.png')
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display.Image('./trpzip2_nglview.png')
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